Faceted classification is based on the principle that information has a multi-dimensional quality, and can be classified in many different ways. Subjects of an informational domain are subdivided into facets (or more simply, categories) to represent this dimensionality. The attributes of the domain are related in facet hierarchies. The materials within the domain are then described and classified based on these attributes.
FIG. 1 illustrates the general approach of faceted classification in the prior art, as it applies (for example) to the classification of wine.
Faceted classification is known as an analytico-synthetic method, as it involves processes of both analysis and synthesis. To devise a scheme for faceted classification, information domains are analyzed to determine their basic facets. The classification must then be synthesized (or built) by applying the attributes of these facets to the domain based on constructive rules.
Overwhelming, faceted classification is a manual activity, practiced by professional classificationists such as librarians and information architects. It is very labor-intensive and intellectually challenging. To ease this complexity, scholars have devised rules and guidelines for faceted classification. This body of scholarship dates back many decades, long before the advent of modern computing and data analysis.
More recently, technology has been enlisted in the service of faceted classification. For example, rule-based categorization tools (or classifiers) are often employed to automate the assignment of attributes to objects within an existing faceted classification scheme. Critically, however, technologies such as these have been applied within the traditional methods of faceted classification.
Modeled within these traditional methods, existing technologies bear some inherent limitations. Chief among these is in the very faceted nature of the resultant structures (illustrated in FIG. 1 as the three facet hierarchies of type, price, and region). Descriptions based on facet hierarchies are inherently fragmented.
Faceted classification schemes enable multiple perspectives, an oft-cited benefit. Unfortunately, when these perspectives are fragmented across multiple hierarchies, they are not intuitive. As the number of facets (or dimensions) in the structure increases, visualization becomes increasingly difficult. Consequently, visualizations of faceted classification schemes are often reduced to “flat”, one-dimensional result sets; structures are navigated across only one facet at a time. This type of reduction obscures the rich complexity of the underlying structure.
Beyond these visualization problems, there are fundamental structural limitations. Again, in a fragmented state, there is no obvious connection between the facets of an information domain. For example, in FIG. 1, it is not clear how the facets of type, price, and region interact to describe wine. The facets provide descriptive value, but they must be connected to serve an explanatory framework.
Once selected, the facets themselves are static and difficult to revise. This represents a considerable risk in the development of a faceted scheme. Classificationists often lack complete knowledge of the information domain, and thus the selection of these organizing bases is prone to error. Under a dynamic system of classification, these risks would be mitigated by the ability to easily add or alter the underlying facets. Traditional methods of faceted classification and derivative technologies lack flexibility at this fundamental level.
Contrasting faceted hierarchies with simple (unitary) hierarchies illuminates these problems. Simple hierarchies are intuitive and easy to visualize. They often integrate many organizing bases (or facets) simultaneously, providing a more holistic perspective of all the relevant attributes. Attributes are coupled across facet boundaries and may be navigated concurrently. By integrating attributes, rather than fragmenting them, they offer a much more economical and robust explanatory framework.
Thus, there are many disadvantages with the current state of the art in automated faceted classification, specifically as they relate to faceted classification synthesis. Technologies are applied within or based on traditional methods. The resultant structures are inherently fragmented, posing problems of visualization, integration, and holistic perspective.
Methods and technologies are needed that combine the expressiveness and flexibility of faceted schemes within integrated and richly descriptive hierarchies. Moreover, this flexibility must extend down to the fundamental level of the classification scheme itself, in a dynamic construction of facets as organizing bases.